A robust MRI-based brain tumor classification via a hybrid deep learning technique

The brain is the most vital component of the neurological system. Therefore, brain tumor classification is a very challenging task in the field of medical image analysis. There has been a qualitative leap in the field of artificial intelligence, deep learning, and their medical imaging applications in the last decade. The importance of this remarkable development has emerged in the field of biomedical engineering due to the sensitivity and seriousness of the issues related to it. The use of deep learning in the field of detecting and classifying tumors in general and brain tumors in particular using magnetic resonance imaging (MRI) is a crucial factor in the accuracy and speed of diagnosis. This is due to its great ability to deal with huge amounts of data and avoid errors resulting from human intervention. The aim of this research is to develop an efficient automated approach for classifying brain tumors to assist radiologists instead of consuming time looking at several images for a precise diagnosis. The proposed approach is based on 3064 T1-weighted contrast-enhanced brain MR images (T1W-CE MRI) from 233 patients. In this study, the proposed system is based on the results of five different models to use the combined potential of multiple models, trying to achieve promising results. The proposed system has led to a significant improvement in the results, with an overall accuracy of 99.31%.